Publication Type:

Journal Article

Source:

Advances in Intelligent Systems and Computing, Springer Verlag, Volume 397, p.105-116 (2016)

ISBN:

9788132226697

URL:

http://www.scopus.com/inward/record.url?eid=2-s2.0-84955270932&partnerID=40&md5=9c33a4053e17116e0cda3065efffb721

Keywords:

Adaptive boosting, Agglomerative clustering, Algorithms, Artificial intelligence, Bagging, Boosting, Clustering algorithms, Decision trees, Divisive clustering, Hier-archical clustering, Intelligent systems, K-means clustering, Learning algorithms, Learning systems, Logistic regressions, Partitional clustering, Problem solving, Random forests, Soft computing, Supervised learning, Unsupervised learning

Abstract:

<p>Artificial Intelligence, a field which deals with the study and design of systems, which has the capability of observing its environment and does functionalities which aims at maximizing the probability of its success in solving problems. AI turned out to be a field which captured wide interest and attention from the scientific world, so that it gained extraordinary growth. This in turn resulted in the increased focus on a field—which deals with developing the underlying conjectures of learning aspects and learning machines—machine learning. The methodologies and objectives of machine learning played a vital role in the considerable progress gained by AI. Machine learning aims at improving the learning capabilities of intelligent systems. This survey is aimed at providing a theoretical insight into the major algorithms that are used in machine learning and the basic methodology followed in them. © Springer India 2016.</p>

Notes:

cited By 0; Conference of International Conference on Soft Computing Systems, ICSCS 2015 ; Conference Date: 20 April 2015 Through 21 April 2015; Conference Code:160689

Cite this Research Publication

A. Sankar, Bharathi, P. D., Midhun, M., Vijay, K., and Kumar, TaSenthil, “A conjectural study on machine learning algorithms”, Advances in Intelligent Systems and Computing, vol. 397, pp. 105-116, 2016.